Video as a set or sequence of frames. Later, other approaches were developed using multiple RGB frames for video classification. These approaches sparsely sample multiple frames from the video, which are applied to a 2D cnn followed by a late integration of frame-level features using average pooling [1]}, multilayer perceptrons [2]}, recurrent aggregation [3]}, [4]}, or attention [5]}, [6]}. To boost performance, most of these approaches also combine video frame sequence with externally computed optical flow. This shows to be helpful, but computationally intensive.
